In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. Thisb book develops de next task with models: Nonlinear Black-Box Model Identification Nonlinear Model Identification Fit Nonlinear Models Identifying Nonlinear ARX Models Nonlinearity Estimators for Nonlinear ARX Models Estimate Nonlinear ARX Models in the GUI Estimate Nonlinear ARX Models at the Command Line Validating Nonlinear ARX Models Identifying Hammerstein-Wiener Models Nonlinearity Estimators for Hammerstein-Wiener Models Estimation Algorithm for Hammerstein-Wiener Models Validating Hammerstein-Wiener Models Linear Approximation of Nonlinear Black-Box Models ODE Parameter Estimation (Grey-Box Modeling) Estimating Linear Grey-Box Models Estimating Nonlinear Grey-Box Models After Estimating Grey-Box Models Estimating Coefficients of ODEs to Fit Given Solution Estimate Model Using Zero/Pole/Gain Parameters Time Series Identification Estimating Time-Series Power Spectra Estimate Time-Series Power Spectra Using the GUI Estimate Time-Series Power Spectra at the Command Line Estimating AR and ARMA Models Estimating Polynomial Time-Series Models in the GUI Estimating AR and ARMA Models at the Command Line Estimating State-Space Time-Series Models Estimating State-Space Models at the Command Line Identify Time-Series Models at Command Line Estimating Nonlinear Models for Time-Series Data Estimating ARIMA Models Analyzing of Time-Series Models Recursive Model Identification General Form of Recursive Estimation Algorithm Kalman Filter Algorithm Recursive Estimation and Data Segmentation Techniques in System Identification Toolbox Model Analysis Validating Models After Estimation Plotting Models in the GUI Simulating and Predicting Model Output Simulation and Prediction in the GUI Simulation and Prediction at the Command Line Predict Using Time-Series Model Residual Analysis Impulse and Step Response Plots Frequency Response Plots Displaying the Confidence Interval Noise Spectrum Plots Pole and Zero Plots Analyzing MIMO Models Akaike's Criteria for Model Validation Troubleshooting Models Unstable Models Missing Input Variables Complicated Nonlinearities Spectrum Estimation Using Complex Data System Identification Toolbox Blocks Using System Identification Toolbox Blocks in Simulink Models Identifying Linear Models Simulating Identified Model Output in Simulink Simulate Identified Model Using Simulink Software System Identification Tool GUI